Update CUDA docs to use k3s suggested method (#1430)

docs/usage/advanced/cuda.md

@@ -25,24 +25,12 @@ To get around this we need to build the image with a supported base image.
 This Dockerfile is based on the [K3s Dockerfile](https://github.com/rancher/k3s/blob/master/package/Dockerfile)
 The following changes are applied:
 
-1. Change the base images to nvidia/cuda:11.2.0-base-ubuntu18.04 so the NVIDIA Container Runtime can be installed. The version of `cuda:xx.x.x` must match the one you're planning to use.
-2. Add a custom containerd `config.toml` template to add the NVIDIA Container Runtime. This replaces the default `runc` runtime
-3. Add a manifest for the NVIDIA driver plugin for Kubernetes
-
-### Configure containerd
-
-We need to configure containerd to use the NVIDIA Container Runtime. We need to customize the config.toml that is used at startup. K3s provides a way to do this using a [config.toml.tmpl](cuda/config.toml.tmpl) file. More information can be found on the [K3s site](https://rancher.com/docs/k3s/latest/en/advanced/#configuring-containerd).
-
-```go
-{%
-  include-markdown "./cuda/config.toml.tmpl"
-  comments=false
-%}
-```
+1. Change the base images to nvidia/cuda:12.4.1-base-ubuntu22.04 so the NVIDIA Container Toolkit can be installed. The version of `cuda:xx.x.x` must match the one you're planning to use.
+2. Add a manifest for the NVIDIA driver plugin for Kubernetes with an added RuntimeClass definition. See [k3s documentation](https://docs.k3s.io/advanced#nvidia-container-runtime-support).
 
 ### The NVIDIA device plugin
 
-To enable NVIDIA GPU support on Kubernetes you also need to install the [NVIDIA device plugin](https://github.com/NVIDIA/k8s-device-plugin). The device plugin is a deamonset and allows you to automatically:
+To enable NVIDIA GPU support on Kubernetes you also need to install the [NVIDIA device plugin](https://github.com/NVIDIA/k8s-device-plugin). The device plugin is a daemonset and allows you to automatically:
 
 * Expose the number of GPUs on each nodes of your cluster
 * Keep track of the health of your GPUs
@@ -55,19 +43,34 @@ To enable NVIDIA GPU support on Kubernetes you also need to install the [NVIDIA
 %}
 ```
 
+Two modifications have been made to the original NVIDIA daemonset:
+
+1. Added RuntimeClass definition to the YAML frontmatter.
+
+   ```yaml
+   apiVersion: node.k8s.io/v1
+   kind: RuntimeClass
+   metadata:
+     name: nvidia
+   handler: nvidia
+   ```
+
+2. Added `runtimeClassName: nvidia` to the Pod spec.
+
+Note: you must explicitly add `runtimeClassName: nvidia` to all your Pod specs to use the GPU. See [k3s documentation](https://docs.k3s.io/advanced#nvidia-container-runtime-support).
+
 ### Build the K3s image
 
 To build the custom image we need to build K3s because we need the generated output.
 
 Put the following files in a directory:
 
 * [Dockerfile](cuda/Dockerfile)
-* [config.toml.tmpl](cuda/config.toml.tmpl)
 * [device-plugin-daemonset.yaml](cuda/device-plugin-daemonset.yaml)
 * [build.sh](cuda/build.sh)
 * [cuda-vector-add.yaml](cuda/cuda-vector-add.yaml)
 
-The `build.sh` script is configured using exports & defaults to `v1.21.2+k3s1`. Please set at least the `IMAGE_REGISTRY` variable! The script performs the following steps builds the custom K3s image including the nvidia drivers.
+The `build.sh` script is configured using exports & defaults to `v1.28.8+k3s1`. Please set at least the `IMAGE_REGISTRY` variable! The script performs the following steps builds the custom K3s image including the nvidia drivers.
 
 [build.sh](cuda/build.sh):
 
@@ -108,10 +111,6 @@ Done
 
 If the `cuda-vector-add` pod is stuck in `Pending` state, probably the device-driver daemonset didn't get deployed correctly from the auto-deploy manifests. In that case, you can apply it manually via `#!bash kubectl apply -f device-plugin-daemonset.yaml`.
 
-## Known issues
-
-* This approach does not work on WSL2 yet. The NVIDIA driver plugin and container runtime rely on the NVIDIA Management Library (NVML) which is not yet supported. See the [CUDA on WSL User Guide](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#known-limitations).
-
 ## Acknowledgements
 
 Most of the information in this article was obtained from various sources:
@@ -126,3 +125,4 @@ Most of the information in this article was obtained from various sources:
 * [@markrexwinkel](https://github.com/markrexwinkel)
 * [@vainkop](https://github.com/vainkop)
 * [@iwilltry42](https://github.com/iwilltry42)
+* [@dbreyfogle](https://github.com/dbreyfogle)

docs/usage/advanced/cuda/Dockerfile

@@ -1,39 +1,22 @@
-ARG K3S_TAG="v1.21.2-k3s1"
-FROM rancher/k3s:$K3S_TAG as k3s
-
-FROM nvidia/cuda:11.2.0-base-ubuntu18.04
-
-ARG NVIDIA_CONTAINER_RUNTIME_VERSION
-ENV NVIDIA_CONTAINER_RUNTIME_VERSION=$NVIDIA_CONTAINER_RUNTIME_VERSION
-
-RUN echo 'debconf debconf/frontend select Noninteractive' | debconf-set-selections
-
-RUN apt-get update && \
-    apt-get -y install gnupg2 curl
-
-# Install NVIDIA Container Runtime
-RUN curl -s -L https://nvidia.github.io/nvidia-container-runtime/gpgkey | apt-key add -
+ARG K3S_TAG="v1.28.8-k3s1"
+ARG CUDA_TAG="12.4.1-base-ubuntu22.04"
 
-RUN curl -s -L https://nvidia.github.io/nvidia-container-runtime/ubuntu18.04/nvidia-container-runtime.list | tee /etc/apt/sources.list.d/nvidia-container-runtime.list
-
-RUN apt-get update && \
-    apt-get -y install nvidia-container-runtime=${NVIDIA_CONTAINER_RUNTIME_VERSION}
-
-COPY --from=k3s / /
-
-RUN mkdir -p /etc && \
-    echo 'hosts: files dns' > /etc/nsswitch.conf
-
-RUN chmod 1777 /tmp
+FROM rancher/k3s:$K3S_TAG as k3s
+FROM nvcr.io/nvidia/cuda:$CUDA_TAG
 
-# Provide custom containerd configuration to configure the nvidia-container-runtime
-RUN mkdir -p /var/lib/rancher/k3s/agent/etc/containerd/
+# Install the NVIDIA container toolkit
+RUN apt-get update && apt-get install -y curl \
+    && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
+    && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
+      sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
+      tee /etc/apt/sources.list.d/nvidia-container-toolkit.list \
+    && apt-get update && apt-get install -y nvidia-container-toolkit \
+    && nvidia-ctk runtime configure --runtime=containerd
 
-COPY config.toml.tmpl /var/lib/rancher/k3s/agent/etc/containerd/config.toml.tmpl
+COPY --from=k3s / / --exclude=/bin
+COPY --from=k3s /bin /bin
 
 # Deploy the nvidia driver plugin on startup
-RUN mkdir -p /var/lib/rancher/k3s/server/manifests
-
 COPY device-plugin-daemonset.yaml /var/lib/rancher/k3s/server/manifests/nvidia-device-plugin-daemonset.yaml
 
 VOLUME /var/lib/kubelet

docs/usage/advanced/cuda/build.sh

@@ -2,20 +2,18 @@
 
 set -euxo pipefail
 
-K3S_TAG=${K3S_TAG:="v1.21.2-k3s1"} # replace + with -, if needed
+K3S_TAG=${K3S_TAG:="v1.28.8-k3s1"} # replace + with -, if needed
+CUDA_TAG=${CUDA_TAG:="12.4.1-base-ubuntu22.04"}
 IMAGE_REGISTRY=${IMAGE_REGISTRY:="MY_REGISTRY"}
 IMAGE_REPOSITORY=${IMAGE_REPOSITORY:="rancher/k3s"}
-IMAGE_TAG="$K3S_TAG-cuda"
+IMAGE_TAG="$K3S_TAG-cuda-$CUDA_TAG"
 IMAGE=${IMAGE:="$IMAGE_REGISTRY/$IMAGE_REPOSITORY:$IMAGE_TAG"}
 
-NVIDIA_CONTAINER_RUNTIME_VERSION=${NVIDIA_CONTAINER_RUNTIME_VERSION:="3.5.0-1"}
-
 echo "IMAGE=$IMAGE"
 
-# due to some unknown reason, copying symlinks fails with buildkit enabled
-DOCKER_BUILDKIT=0 docker build \
+docker build \
   --build-arg K3S_TAG=$K3S_TAG \
-  --build-arg NVIDIA_CONTAINER_RUNTIME_VERSION=$NVIDIA_CONTAINER_RUNTIME_VERSION \
+  --build-arg CUDA_TAG=$CUDA_TAG \
   -t $IMAGE .
 docker push $IMAGE
 echo "Done!"

docs/usage/advanced/cuda/cuda-vector-add.yaml

@@ -3,6 +3,7 @@ kind: Pod
 metadata:
   name: cuda-vector-add
 spec:
+  runtimeClassName: nvidia # Explicitly request the runtime
   restartPolicy: OnFailure
   containers:
     - name: cuda-vector-add

docs/usage/advanced/cuda/device-plugin-daemonset.yaml

@@ -1,3 +1,9 @@
+apiVersion: node.k8s.io/v1
+kind: RuntimeClass
+metadata:
+  name: nvidia
+handler: nvidia
+---
 apiVersion: apps/v1
 kind: DaemonSet
 metadata:
@@ -7,35 +13,37 @@ spec:
   selector:
     matchLabels:
       name: nvidia-device-plugin-ds
+  updateStrategy:
+    type: RollingUpdate
   template:
     metadata:
-      # Mark this pod as a critical add-on; when enabled, the critical add-on scheduler
-      # reserves resources for critical add-on pods so that they can be rescheduled after
-      # a failure.  This annotation works in tandem with the toleration below.
-      annotations:
-        scheduler.alpha.kubernetes.io/critical-pod: ""
       labels:
         name: nvidia-device-plugin-ds
     spec:
+      runtimeClassName: nvidia # Explicitly request the runtime
       tolerations:
-      # Allow this pod to be rescheduled while the node is in "critical add-ons only" mode.
-      # This, along with the annotation above marks this pod as a critical add-on.
-      - key: CriticalAddonsOnly
+      - key: nvidia.com/gpu
         operator: Exists
+        effect: NoSchedule
+      # Mark this pod as a critical add-on; when enabled, the critical add-on
+      # scheduler reserves resources for critical add-on pods so that they can
+      # be rescheduled after a failure.
+      # See https://kubernetes.io/docs/tasks/administer-cluster/guaranteed-scheduling-critical-addon-pods/
+      priorityClassName: "system-node-critical"
       containers:
-      - env:
-        - name: DP_DISABLE_HEALTHCHECKS
-          value: xids
-        image: nvidia/k8s-device-plugin:1.11
+      - image: nvcr.io/nvidia/k8s-device-plugin:v0.15.0-rc.2
         name: nvidia-device-plugin-ctr
+        env:
+          - name: FAIL_ON_INIT_ERROR
+            value: "false"
         securityContext:
-          allowPrivilegeEscalation: true
+          allowPrivilegeEscalation: false
           capabilities:
             drop: ["ALL"]
         volumeMounts:
-          - name: device-plugin
-            mountPath: /var/lib/kubelet/device-plugins
-      volumes:
         - name: device-plugin
-          hostPath:
-            path: /var/lib/kubelet/device-plugins
+          mountPath: /var/lib/kubelet/device-plugins
+      volumes:
+      - name: device-plugin
+        hostPath:
+          path: /var/lib/kubelet/device-plugins